Bibliographic record
Abstract
Although clinical diagnosis of pulmonary embolism (PE) is not sufficiently reliable to determine management, it is valuable for stratifying patients into high, intermediate, and low clinical suspicion of embolism. Clinical assessment can then be combined with lung scanning to identify groups of patients with a sufficiently high or low probability of PE that a decision to anticoagulate or withhold therapy can be made. Approximately half of patients with suspected PE will fall into one of these categories. Thrombosis in the deep veins of the leg (DVT) can be detected by noninvasive tests in approximately 50%, and by bilateral venography in approximately 70% of patients with PE, and provides grounds for anticoagulation of some patients with nondiagnostic combinations of clinical and lung scan assessments. Failure to detect DVT makes it less likely but does not exclude the possibility that the patient had a PE. Preliminary evidence suggests that the majority of patients with nondiagnostic combinations of clinical assessment, lung scanning, and negative noninvasive tests for DVT can safely be managed without anticoagulation, provided serial noninvasive tests for DVT remain normal over a 2-week period. Pulmonary angiography may be advisable in patients with nondiagnostic combinations of the above tests in whom (a) the probability of PE remains high (e.g. 30-80%), (b) cardiopulmonary reserve is poor, (c) serial follow-up is not feasible, or (d) future management (e.g. subsequent pregnancy) would be influenced by the result. D-Dimer measurements are sensitive but nonspecific for PE and therefore may have a high negative predictive value, further simplifying the diagnostic approach to PE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".